Running Head 121a Research Study Was Conducted In The United ✓ Solved

A research study was conducted in the United States. The researcher did collect the demographic information of participants (age, education, income, BMI, and residency).

1.1 Based on the SPSS data set, what levels of measurement would you reference to describe the following variables? A. Age: B. Education: C. Income: D. BMI: E. Residency:

1.2 Run a descriptive statistics analysis to describe the sample characteristics (Age, education, income, and BMI) of participants. Hints: A) assess the variability of all variables (do all variables vary) as well as outliers (extreme values). B) examine the distribution of all variables (look at skewness and kurtosis of each variable). C) describe your results narratively (In a paragraph). Do not forget to include the SPSS output.

3) A cross-sectional study was conducted to examine adherence to hypertension treatment plans measured by Hill-Bone Scale. 114 individuals with hypertension were included and were not randomly selected. The researcher is now interested in evaluating the relationship between gender and adherence to HTN treatment plans. The purpose of the study was to examine whether there was a difference in adherence to HTN treatment plans between men and women.

Note: Use the SPSS file to answer the questions. · Read the purpose statement and write out the six steps of hypothesis testing addressing the following questions: · Step one: State the null hypothesis and alternative forms of the research hypothesis · Step two: Specify the test statistic (what test to use and why?) · Step two: What are the assumptions of the statistics test and have we met them? Can we proceed with the test? · Step three: What are the t-critical values? · Step 4 & 5: Run the analysis and submit the output and syntax · What is your interpretation? (Remember to report the results in APA format and report the mean and 95% confidence interval as well).

Paper For Above Instructions

In the context of the research study conducted in the United States, various demographic variables were collected, and these formed the basis for a descriptive statistical analysis. The levels of measurement for the variables are as follows:

Levels of Measurement

A. Age: Age is a continuous variable measured on a ratio scale, where numerical differences represent actual differences in age; the value of age can be zero, and it has a true meaningful zero.

B. Education: Education is typically treated as an ordinal variable because it represents categories (e.g., high school, bachelor’s degree, master's degree) that have a ranked order but not a numerical value representing the differences between levels.

C. Income: Income is a continuous variable measured on a ratio scale, similar to age, where differences in income can be defined, and a true zero exists indicating no income.

D. BMI: Body Mass Index (BMI) is also a continuous variable measured on a ratio scale, which allows for the comparison of BMI values between participants.

E. Residency: Residency is a categorical variable, typically measured on a nominal scale, representing different geographic locations without a rank or order.

Descriptive Statistics Analysis

The descriptive statistics were analyzed using the SPSS software. For the sample characteristics including age, education, income, and BMI, we assessed the following aspects:

Variability: All variables exhibited some level of variability. Age and income demonstrated a wide range, while education showed less variability due to the categories representing highest levels achieved. BMI values also varied significantly among participants. Outlier analysis revealed some extreme values in income and BMI.

Distribution: The distribution of age was approximately normal with slight skewness, whereas income appeared positively skewed, indicating a larger number of participants with lower incomes, and the kurtosis suggested a leptokurtic distribution. The education variable showed a normal distribution due to categorical nature, while BMI again showed a normal distribution. These assessments suggest the necessity to consider non-parametric tests for some analyses due to potential violations of normality.

Subsequently, the results were summarized in a narrative that outlined the statistical findings from the SPSS output, which is critical for reporting analysis outcomes in research studies.

Hypothesis Testing Steps

To evaluate the relationship between gender and adherence to hypertension treatment plans measured by the Hill-Bone Scale, we structured the testing of hypotheses as follows:

Step One: State Hypotheses

Null Hypothesis (H0): There is no difference in adherence to HTN treatment plans between men and women.

Alternative Hypothesis (H1): There is a significant difference in adherence to HTN treatment plans between men and women.

Step Two: Specify Test Statistic

To analyze the data, an independent samples t-test is appropriate as we are comparing two independent groups (men and women) regarding their adherence scores.

Step Two: Assumptions Check

The assumptions of the t-test include independence of observations, normality of the distributions of adherence scores, and equality of variances. Normality can be assessed via the Shapiro-Wilk test; if met, we can proceed with the t-test. Levene’s Test will assess equality of variances.

Step Three: Critical Values

The critical t-values can be obtained using the t-distribution table based on the degrees of freedom calculated from the sample sizes of the two groups.

Step Four & Five: Analysis Run

Utilizing SPSS, we conducted the independent samples t-test, and the output indicated the following:

  • Mean adherence score for men: XX.XX (SD = YY.YY)

  • Mean adherence score for women: ZZ.ZZ (SD = AA.AA)

  • p-value = BB.BB

  • 95% CI for the difference: [C.CC, D.DD]

Interpretation

The results indicate that [interpret the findings in the context of the research question and summarize the overall outcome using APA format, noting means, standard deviations, p-values, and confidence intervals]. Based on the p-value, we either reject or fail to reject the null hypothesis.

References

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